| import gradio as gr |
| import nltk |
| import joblib |
|
|
| nltk.download("punkt") |
| nltk.download("wordnet") |
| nltk.download('punkt_tab') |
|
|
| model = joblib.load("sentiment_pipeline.joblib") |
|
|
| def predict_sentiment(text, show_preprocessed=False): |
| proba = model.predict_proba([text])[0] |
| sentiment = "Positive 😀" if proba[1] >= 0.5 else "Negative 😞" |
| confidence = f"{round(max(proba) * 100, 2)}%" |
| pre_out = "" |
| if show_preprocessed: |
| pre_out = model.named_steps["textpreprocessor"].transform([text])[0] |
| return sentiment, confidence, pre_out |
|
|
| with gr.Blocks(css=""" |
| .pipeline-container { |
| background-color: blue; |
| border:1px solid #ddd; |
| border-radius:8px; |
| padding:8px; |
| margin-bottom: 4px; |
| } |
| .footer { |
| margin-top: 24px; |
| font-size:0.9rem; |
| text-align:center; |
| } |
| """) as demo: |
|
|
| gr.Markdown("# 🎬 SentiMDB") |
| gr.Markdown( |
| "### SentiMDB is a lightweight, production-ready Sentiment Analysis Pipeline based on IMDb movie reviews. It features a Flask web app, a Dockerized setup for easy deployment, and a Hugging Face Spaces-powered online demo. The project includes a comprehensive Jupyter Notebook, offering a guide to English Text Preprocessing and detailing the full Machine Learning Development process, including Model Selection, Error Analysis, and Fine-Tuning. By leveraging classic machine learning tools alone, the model achieved 91.67% prediction accuracy." |
| ) |
| |
|
|
| with gr.Row(): |
| with gr.Column(scale=1): |
|
|
| |
| gr.HTML(""" |
| <div class="pipeline-container"> |
| <h4 style="text-align:center; margin:0 0 8px 0;">Pipeline</h4> |
| <div style="display:flex; justify-content:space-around; align-items:center;"> |
| <div>📝 Input Text</div> |
| <div>→</div> |
| <div>🔧 TextPreprocessor</div> |
| <div>→</div> |
| <div>📊 TF‑IDF</div> |
| <div>→</div> |
| <div>🤖 Logistic Regressor</div> |
| </div> |
| </div> |
| """) |
|
|
| |
| |
| review = gr.Textbox(lines=3, placeholder="Type your movie review here…") |
| show_pre = gr.Checkbox(label="Show Preprocessed text", value=True) |
| analyze_btn = gr.Button("Analyze", variant="primary") |
|
|
| |
|
|
| with gr.Column(scale=1): |
| |
| sentiment_out = gr.Label(label="Sentiment") |
| confidence_out = gr.Textbox(label="Confidence") |
| pre_out = gr.Textbox(label="Preprocessed Text", interactive=False) |
|
|
| |
|
|
| |
| analyze_btn.click( |
| fn=predict_sentiment, |
| inputs=[review, show_pre], |
| outputs=[sentiment_out, confidence_out, pre_out] |
| ) |
|
|
| |
| gr.HTML( |
| '<div class="footer">For the full project Jupyter Notebook, Flask Web App & Docker Config, visit: <a href="https://github.com/Hoom4n/SentiMDB" target="_blank">https://github.com/Hoom4n/SentiMDB</a></div>' |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
